Preset free imaging for ultrasound device

ABSTRACT

Aspects of the disclosed technology provide ways to detect the object of ultrasound scanning and to automatically, load system settings and image preferences necessary to generate high quality output images. In some aspects, an ultrasound system can be configured to perform steps including receiving a selection of a first transducer, identifying a body structure or organ based on a signal received in response to an activation of the first transducer, retrieving a first set of image parameters corresponding with the body structure, and configuring the first transducer based on the first set of image parameters. Methods and machine-readable media are also provided.

TECHNICAL FIELD

This disclosure relates to ultrasound imaging and in particular, forways to automatically identifying an organ or body part being scannedand selecting optimal image parameters based on the same.

BACKGROUND OF THE INVENTION

Ultrasound imaging is a widely used for examining a wide range ofmaterials and objects across an array of different applications.Ultrasound imaging provides an effective tool for analyzing materialsand objects in a non-invasive manner. As a result, ultrasound imaging isespecially common in the practice of medicine as an ailment diagnosis,treatment, and prevention tool. Specifically, because of its relativelynon-invasive nature, low cost and fast response time ultrasound imagingis widely used throughout the medical industry to diagnose and preventailments. Further, as ultrasound imaging is based on non-ionizingradiation it does not carry the same risks as other diagnosis imagingtools, such as X-ray imaging or other types of imaging systems that useionizing radiation.

Ultrasound imaging is accomplished by generating and directingultrasonic sound waves into a material of interest, first in a transmitphase and subsequently in a receive phase. During the transmit phase, anultrasonic signal is transmitted into a material of interest by applyingcontinuous or pulsed electronic signals. During the receive phase,reflections generated by boundaries between dissimilar materials arereceived by receiving devices, such as transducers. The reflections arethen converted to electrical signals that can then be processed todetermine the locations of echo sources. The resulting data can be usedto produce interior images of an object or organ of interest, e.g. bydisplaying images using a display device, such as a monitor.

Ultrasound imaging can offer a wealth of clinical information.Specifically, ultrasound imaging can be used in abdominal ultrasound (tovisualize abdominal tissues and organs), bone sonometry (to assess bonefragility), breast ultrasound (to visualize breast tissue), dopplerfetal heart rate monitors (to listen to a fetal heart beat), dopplerultrasound (to visualize blood flow through a blood vessel, organs, orother structures), echocardiogram (to view a heart), fetal ultrasound(to view a fetus in pregnancy), ultrasound-guided biopsies (to collect asample of tissue), ophthalmic ultrasound (to visualize ocularstructures) and ultrasound-guided needle placement (in blood vessels orother tissues of interest). Ultrasound imaging has also been used indescribing various disease states, such as diseases of the liver,breast, prostate, thyroid or other organs through single measurements ofstiffness or shear wave velocity.

Typically ultrasound systems include a main processing console and anultrasound transducer. The ultrasound transducer can include one or moretransducers (e.g., a transducer array) that is typically positioned awayfrom the main console and controlled by a user/operator in gatheringultrasound image data.

In conventional operation, the user/operator is responsible foradjusting transducer parameters, and optimizing the image outputsettings to produce optimal images. Because of the numerous variablesattendant in different patients with various disease states (e.g.,weight, fat content, tissue density, etc.), it is difficult formanufacturers to pre-program optimal transducer parameters and imagesettings. Therefore, a need exists to provide a way to automaticallyoptimize ultrasound (back-end) and image (front-end parameters) toproduce optimal ultrasound images while minimizing setup time.

SUMMARY

Aspects of the disclosed technology provide an ultrasound imaging systemthat includes one or more processors, a plurality of transducers coupledto the one or more processors, and a non-transitory computer-readablemedium coupled to the processors. The computer-readable medium includesinstructions to cause the processors to perform operations including:receiving a selection of a first transducer, identifying a bodystructure based on a signal received in response to an activation of thefirst transducer, and retrieving a first set of image parameterscorresponding with the body structure. In some aspects, the instructionscan further cause the processors to perform operations for configuringthe first transducer based on the first set of image parameters,collecting a first image from the body structure via the firsttransducer, using the first set of image parameters, and analyzing oneor more properties of the first image to determine if any additionalimage parameters need to be updated.

In another aspect, the disclosed technology provides a method foroptimizing ultrasound/sonogram images that includes steps for: receivinga selection of a first transducer, wherein the first transducer ispositioned on a body structure, identifying the body structure, andretrieving a first set of image parameters corresponding with the bodystructure. In some aspects, the method can further include steps forconfiguring the first transducer based on the first set of imageparameters, collecting a first image from the body structure via thefirst transducer, using the first set of image parameters, and analyzingone or more properties of the first image to determine if any additionalimage parameters need to be updated.

In yet another aspect, the disclosed technology provides anon-transitory computer-readable storage medium (e.g., a programproduct) that is configured to cause an imaging system to performoperations for receiving a selection of a first transducer, identifyinga body structure based on a signal received in response to an activationof the first transducer, and retrieving a first set of image parameterscorresponding with the body structure. In some aspects, the operationsfurther include configuring the first transducer based on the first setof image parameters, collecting a first image from the body structurevia the first transducer, using the first set of image parameters, andanalyzing one or more properties of the first image to determine if anyadditional image parameters need to be updated.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of an ultrasound system that can be usedto implement image optimization techniques of the disclosed technology.

FIG. 2 illustrates steps of an example process for determiningultrasound operator imaging preferences.

FIG. 3 is a block diagram of an example process for automaticallyidentifying an object/organ to be scanned and loading/updating one ormore corresponding imaging parameters and settings.

FIG. 4 conceptually illustrates various layers of a machine-learning(ML) model that can be used to automatically detect an object/organ thatis being scanned.

FIG. 5 illustrates an example of an image processing path that can beused to implement an ultrasound system of the disclosed technology.

FIG. 6 is a block diagram of an example computing device that can beused to implement an automatic tissue/organ detection process, includingthe loading and updated of image parameters and settings, according tosome aspects of the subject disclosure.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology can bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a more thoroughunderstanding of the subject technology. However, it will be clear andapparent that the subject technology is not limited to the specificdetails set forth herein and may be practiced without these details. Insome instances, structures and components are shown in block diagramform in order to avoid obscuring the concepts of the subject technology.

Aspects of the disclosed technology address the aforementionedlimitations of conventional ultrasound imaging systems by providing waysto automatically identify an anatomical region being scanned, andadaptively modify image system settings to obtain images aligned withthe user/operator's aesthetic preferences. The automatic imageoptimization technology provides multiple advantages, including improvedexam quality, consistency, and efficiency, as the system eliminates theneed for an operator to perform time-consuming preset searching andadjustment. Eliminating the need for an operator to select image presetsis not only advantageous for the system user/operator, but also providesa significant improvement for system developers. For example, aconventional high end ultrasound system typically supports around 15 to25 transducers, each of which can have up to 50 presets and supportaround 5 different modes of operation. As a result, theacoustic/clinical team typically needs to optimize around 5,000 imagemodes, requiring the setting/tuning of several hundred parameters. Suchcalibrations can typically take up to one day per mode; therefore,bringing up a new system can take on the order of 20 person-years(assuming a 250 day work-year). The ability to reduce ultrasound systemcalibration to an automatic process not only benefits the users but alsodevelopment companies, enabling them to spend more time on systemdevelopment, while lowering development costs.

Aspects of the disclosed technology provide methods for automaticallyidentifying/classifying a region of anatomy being scanned, and forautomatically optimizing image preset parameters. Classification of theportions of anatomy being scanned can be performed using a machinelearning (ML) classifier, such as a multilevel perceptron neuralnetwork. Once the anatomy is correctly identified, preset systemparameters, such as anatomy-specific transducer presets, areautomatically selected and used when generating output images. Thegeneration of output images can be additionally informed byuser/operator image preferences that can be calibrated by the user uponsystem initialization or startup.

In some aspects, image preferences can be learned on a user-by-userbasis, for example, based on user selections and feedback that indicatetheir aesthetic preferences. In some embodiments, user image preferencescan be determined by a process in which the user is shown a series ofcomparison images and prompted select those that best suit their liking.Comparison images can be provided that cover a full range of modalitiesand organs, as image preferences can depend on what the user is viewing,as well as what imaging modality is used. For example, basic imagescould be of a B-mode variety but they might vary in the type of B-modelike harmonic imaging, spatial compounding, frequency compounding andthe like. A more detailed description of the processes used foridentifying anatomical targets, and for optimizing output images isdiscussed with respect to FIGS. 2 and 3, below.

Turning now to FIG. 1, which illustrates ultrasound system 100 in whichvarious aspects of the disclosed technology can be implemented.Ultrasound system 100 is provided as an example system and in variousembodiments, can have greater (or fewer) components than illustrated inFIG. 1. Ultrasound system 100 can be an ultrasound system where thereceive array focusing unit is referred to as a beam former 102, andimage formation can be performed on a scanline-by-scanline basis.

System control can be centered in the master controller 104, whichaccepts operator inputs through an operator interface and in turncontrols various subsystems. For each scan line, transmitter 106generates a radio-frequency (RF) excitation voltage pulse waveform andapplies it with appropriate timing across the transmit aperture (definedby a sub-array of active elements) to generate a focused acoustic beamalong the scan line. RF echoes received by aperture 108 of transducer110 are amplified and filtered by receiver 108, and then fed into beamformer 102, whose function is to perform dynamic receive focusing, i.e.,to re-align the RF signals that originate from the same locations alongvarious scan lines.

Image processor 112 can perform processing specific to active imagingmode(s) including 2D scan conversion that transforms the image data froman acoustic line grid to an X-Y pixel image for display. For SpectralDoppler mode, image processor 112 can perform wall filtering followed byspectral analysis of Doppler-shifted signal samples using a slidingFFT-window. Image processor 112 can also generate the stereo audiosignal output corresponding to forward and reverse flow signals. Incooperation with master controller 104, image processor 112 also canformat images from two or more active imaging modes, including displayannotation, graphics overlays and replay of cine loops and recordedtimeline data.

Cine buffer 114 provides resident digital image storage for single imageor multiple image loop review, and acts as a buffer for transfer ofimages to digital archival devices. Video images at the end of the dataprocessing path can be stored to the cine memory 114. Instate-of-the-art systems, amplitude-detected, beam formed data may alsobe stored in cine memory 114. For spectral Doppler, wall-filtered,baseband Doppler 1/Q data for a user-selected range gate can be storedin cine memory 114. Subsequently, display 116 can display ultrasoundimages created by image processor 112 and/or images using data stored incine memory 114.

Beam former 102, master controller 104, image processor 112, cine memory114, and display 116 can be included as part of a main processingconsole 118 of ultrasound system 100. In various embodiments, mainprocessing console 118 can include more or fewer components orsubsystems. Transducer 110 can be incorporated in an apparatus that isseparate from the main processing console 118, in a separate apparatusthat is wired or wirelessly connected to the main processing console118. This allows for easier manipulation of ultrasound transducer 110when performing specific ultrasound procedures on various anatomicalstructures of a patient. Transducer geometries can vary depending onapplication. By way of example, curved transducers tend to be used forabdominal and OB/GYN applications, as well as for coverage of body areaswhen additional penetration is needed, for example, in very large legs.Linear transducers tend to be used on more superficial structures likethyroid, vascular, and/or arterial structures, and the like. Asunderstood by those of skill in the art, several other types oftransducers may be used, such as, intraoperative, gastro scopes, transesophageal, surgical, catheter, and the like. However, standardtransducer geometries tend to fall under the categories of: curved,linear and/or face.

FIG. 2 illustrates steps of an example process 200 for determiningoperator image preferences. Process 200 begins when instructions areexecuted to place the operator/user into an imaging preferences setupmode (202). The setup mode enables the user to manually set or configureindividual image preferences, either based on a series of aestheticchoice selections, or by manually configuring system settings, such astransducer parameters. As used herein, user image preferences can beaffected by backend parameters, (e.g., transducer or system datacollection settings), or by front end parameters (e.g., imagetransformations applied after image collection). By way of example,output image aesthetics can be affected by gain, dynamic range, speckletexture, tint, color map, CD processing, CD scale, PW wall filter, tint,and the like.

Next, the user is presented with a variety images reflecting differentbackend and front-end parameter configurations (204). In someapproaches, the user is presented with side-by-side images and promptedto select those with the greatest aesthetic appeal. As such, aestheticpreference of the user can be communicated using an A/B image comparisonprocess for specific anatomical regions. To ensure that user preferencesare adequately captured a variety of different image types are displayedto the user, so that user preferences across a variety of image modescan be inferred from the user's selections. By way of example, users canbe prompted to select between various image types, including but notlimited to: B-mode, CD-mode, PW-mode, M-mode, Elastography, 3D/4D, andCEUS images, etc. Different image types can also be displayed forvarious anatomical regions, and can represent a wide variety of imagesettings e.g., for dynamic range, tint, speckle texture, persistence,and map settings, etc., to ensure that the user's preferences are wellcaptured. In some aspects, additional control images may be provided tothe user for further preference learning (206). Once a user'spreferences (profile) is understood by the system, basic parameters canbe extracted and stored into a database (step 208) that can be used toalign image collection and display strategies with those of the userspreferences, as discussed in further detail below with respect to FIG.3.

FIG. 3 illustrates a block diagram of an example process 300 forautomatically identifying an object/organ to be imaged andloading/updating one or more corresponding image parameters andsettings. Process 300 beings with the user selection of a firsttransducer that is positioned on a surface of a body structure, i.e., adiscrete portion of anatomy to be the subject of imaging/scanning (302).Transducer selection can be communicated in a variety of ways,including, the user's selection/engagement with a particular type oftransducer wand or probe apparatus. As discussed above, transducer/probegeometries can vary depending upon the type of anatomy to be scanned. Inother aspects, transducer selection may be provided through userengagement with a control menu on the imaging system.

Next, the imaging system automatically identifies the body structure tobe scanned based on signals received from the first transducer (step304). As discussed in further detail below,identification/classification of the target body structure/anatomy canbe performed using a machine learning classifier, such as a multilayerperceptron neural network. In alternative embodiments, anatomyidentification may also be performed by comparing the scan anatomy witha database of known anatomical features or structures.

Once the body structure has been identified, one or more backendparameters, such as parameters of the first transducer, can be retrievedfrom a database, for example, that correlates, system configurations forperforming image collection with anatomy type (306). Because differentanatomical regions and have different imaging presets, the automaticidentification of the anatomy to be scanned can be used to automate thepreloading and configuration of system image collection and displaysettings, without the need for input from the user (308). After thefirst transducer has been configured based on settings/parametersretrieved in step 306, one or more initial images (e.g. a first image)can be collected from the target anatomy and used to determine if anyadditional parameters need to be updated (310).

The determination of whether or not the initially collected imagesachieve user's desired quality of image output can be performed using anautomatic process, such as by comparing image characteristics with adatabase of known user preferences or previously accepted images.Alternatively, manual input provided from the user can be used todetermine if additional configuration changes are needed. If it isdetermined in step 310 that no additional configuration changes areneeded, process 300 advances to step 314 and the imaging processes isallowed to proceed.

Alternatively if it is determined in step 310 that additional imageparameters need to be updated, process 300 advances to step 312 in whichthe first set of settings parameters are updated to generate a secondset of image parameters. In such aspects, process 312 proceeds (back) tostep 304, iterating the process of body structure identification andimage retrieval (e.g., steps 304-310) until the image output is ofadequate quality and/or indicated as acceptable by the operator/user.

As discussed above, anatomy identification can be performed using amachine learning classifier, such as a multilayer perceptron neuralnetwork discussed in further detail with respect to FIG. 4.

Specifically, FIG. 4 conceptually illustrates various layers of amachine-learning (ML) classifier 400 that can be used to automaticallydetect an object/organ being scanned, as described with respect to FIG.3. Classifier 400 includes four total layers: an input layer 402, afirst hidden layer 404, a second hidden layer 406, and an output layer408. Those of skill in the art would understand that a greater (orfewer) number of layers may be implemented, without departing from thescope of the technology. By way of example, a deep learning approachcontaining a large number of layers may be implemented.

In the illustrated example, input layer 402 is configured to receive anultrasound image (input), and to disaggregate the input into variouspatterns of local contrast, which are then provided as an input to firsthidden layer 404. First hidden layer 404 receives the input from inputlayer 402, and provides an output corresponding with the classificationof specific organ features to second hidden layer 406. In turn, secondhidden layer 406 provides a classification for whole organ types, withthe output being provided to output layer 408, eventually resulting inan anatomy classification for the original input image provided to inputlayer 402. As discussed above, this classification can inform thesystem's retrieval of various presets, including backend settings andsystem parameters, and frontend image transformations.

It is understood that multilevel perceptron neural network architecturesare only one example of a machine learning approach that can beimplemented. In other aspects, different machine learning approaches canbe used. For example, such classifiers can include, but are not limitedto: a Multinomial Naive Bayes classifier, a Bernoulli Naive Bayesclassifier, a Perceptron classifier, a Stochastic Gradient Descent (SGD)Classifier, and/or a Passive Aggressive Classifier, or the like.Additionally, the ML models can be configured to perform various typesof regression, for example, using one or more various regressionalgorithms, including but not limited to: a Stochastic Gradient DescentRegressor, and/or a Passive Aggressive Regressor, etc.

FIG. 5 illustrates an example of an image processing path 500 that canbe used to implement an ultrasound imaging system of the disclosedtechnology. It is understood that imaging path 500 represents oneexample processing pipeline that can be used, however, that a greater orfewer number of functional units may be deployed, depending on thedesired implementation.

Processing path 500 includes a transmitter, waveform, and delaygenerator 502 that is configured to transmit sound waves into abody/organ being imaged. In some aspects, delay generator 502 isconfigured to determine an aperture, delay profile, windowing function,and power of the transmit profile. All these parameters are potentialcandidates for modification of the imaging system, for example, tooptimize image output performance (e.g. back-end parameters, asdiscussed above).

The output of delay generator 502 is connected to transmitter/s 504,that receive input signals and amplifies them to levels needed to drivea transducer. The output of transmitter/s 504 is provided totransmit/receive switch 508, which is configured to allow the output oftransmitter 504 to be connected to transducer 506, while preventing itfrom potentially destroying low-noise amplifier 510. Subsequently,waveforms are emitted from transducer 506, and received by the sametransducer (506) after interacting with the organ/tissue being imaged.These receive signals pass through transmit/receive switch 508, and areamplified by low noise amplifier 510. In some implementations, low noiseamplifier 510 can have several different gain settings that can beconfigured based on the desired imaging mode, as discussed above (e.g.as backend parameters). The output of low noise amplifier 510 is thenprovided to a variable gain amplifier 512. In some aspects, variablegain amplifier 512 is configured to amplify the received input signal ata rate needed to compensate for signal attenuation over time. In someimplementations, the amplification rate of variable gain amplifier 512can be configured/programmed, e.g., by a user or operator of the system.The output of variable gain amplifier 512 is then received by ananalog-to-digital converter 514, for example, to convert the signal froman analog waveform to a digital waveform.

Analog-to-digital converter 514 can be configured to automaticallyadjust the sampling rate. The output of analog-to-digital converter 514is then stored in buffer 516. Once the data has been stored and can beprocessed, it is provided to channel domain pre=processing module 518.Processing model 518 can be configured to work on single transducerelement data, and to process it on a sample-by-sample basis for gain,frequency, bandwidth, and/or decoding, and the like. In someimplementations, processing module 518 may also be configured to processmultiple transmit/receive cycles of data, for example, for dataaveraging, decoding, nonlinear processing, and the like.

Post-processing, the data is then transferred to buffer 520, and routedto image formation module 522. Image formation module 522 is configuredto process the received data to form an image. In some implementations,image formation module 522 can be configured to apply a variety ofprocessing transformations, including, but not limited to a delay, asum, a filter, gain, and various forms of adaptive processing of theabove, etc. Data output from image formation block 522 is then buffered(e.g. in buffer 524) before being transferred to a coherent processingmodule 526.

In some implementations, coherent processing module 526 is configured toperform additional processing, for example, that can include nonlinearsignal extraction, filtering for synthetic aperture processingtechniques, and the like. Data entering and leaving coherent processingmodule 526 has both phase and magnitude information; the data fromcoherent processing module 526 is then passed to a backend processingmodule 528. Backend processing module 528 performs various forms ofsampling, filtering and conversion, including but not limited to:up-sampling, down-sampling, log compression, detection, spatialfiltering, adaptive filtering, scan conversion, and the like, so thatthe data can be displayed on a display ultimately outputted to displaydevice 530.

It is understood that similar processing techniques can be applied todifferent image mode types, without departing from the disclosedtechnology. For example, similar steps may be applied for harmonic mode,contrast enhanced ultrasound mode, spatial compounding, frequencycompounding, and the like. It is also understood that aspects of thedisclosed technology are applicable to processing step or other imagemodes, such as, CD-mode, PW=Doppler, M-mode, color M-mode, etc.

FIG. 6 is a block diagram of an example computing device that can beused to implement an automatic object/organ detection process, includingthe loading and update of image parameters and settings, according tosome aspects of the subject disclosure. Device 610 includes centralprocessing unit (CPU) 662, network interfaces 668, and a bus 615 (e.g.,a PCI bus). When acting under the control of appropriate software orfirmware, the CPU 662 is responsible for executing a method forreceiving a selection of a first transducer, wherein the firsttransducer is positioned on a surface of a body structure, identifyingthe body structure based on a signal received in response to anactivation of the first transducer, and retrieving a first set of imageparameters corresponding with the body structure. In some aspects, CPU662 is further configured for performing operations for configuring thefirst transducer based on the first set of image parameters; andcollecting a first image from the body structure via the firsttransducer, using the first set of image parameters, analyzing one ormore properties of the first image to determine if any additional imageparameters need to be updated.

In some aspects, CPU 662 is further configured for performing operationsfor updating the first set of settings parameters to generate a secondset of image parameters, if it is determined that additional imagesettings need to be updated, configuring the first transducer based onthe second set of image parameters, collecting a second image from thebody structure via the first transducer, using the second set of imageparameters, and analyzing one or more properties of the second image todetermine if any additional image parameters need to be updated.

CPU 662 can accomplish all these functions under the control of softwareincluding an operating system and any appropriate applications software.CPU 662 may include one or more processors 663, such as a processor fromthe INTEL X86 family of microprocessors. In some cases, processors 663can be specially designed hardware for controlling the operations ofimage processing device 610. In some cases a computer-readable memory,e.g., memory 661 (a non-volatile Random Access Memory (RAM), or a ReadOnly Memory (ROM), etc., also forms part of CPU 562. However, there aremany different ways in which memory could be coupled to the system.

Interfaces 668 can be provided as modular interface cards (sometimesreferred to as “line cards”). They can control the sending and receivingof data packets over the network and sometimes support other peripheralsused with image processing device 610. Among the interfaces that may beprovided are Ethernet interfaces, frame relay interfaces, cableinterfaces, Digital Subscriber Line (DSL) interfaces, token ringinterfaces, and the like. In addition, various very high-speedinterfaces may be provided such as fast token ring interfaces, wirelessinterfaces, Ethernet interfaces, Gigabit Ethernet interfaces, ATMinterfaces, High Speed Serial Interfaces (HSSIs), POS interfaces, FDDIinterfaces, WIFI interfaces, 3G/4G/5G cellular interfaces, CAN BUS,LoRA, and the like. Generally, these interfaces may include portsappropriate for communication with the appropriate media. In some cases,they may also include an independent processor and, in some instances,volatile RAM.

Although the system shown in FIG. 6 is one specific image processingdevice of the disclosed embodiments, it is by no means the only devicearchitecture on which aspects of the disclosed technology can beimplemented. For example, an architecture having a single processor thathandles communications as well as routing computations, etc., is oftenused. Further, other types of interfaces and media could also be usedwith image processing device 610.

Regardless of the image processing device's configuration, it may employone or more memories or memory modules (including memory 661) configuredto store program instructions for the general-purpose network operationsand mechanisms for roaming, route optimization and routing functionsdescribed herein. The program instructions may control the operation ofan operating system and/or one or more applications, for example.

Image processing device 610 can also include an application-specificintegrated circuit (ASIC), which can be configured to perform any of theoperations described above. The ASIC can communicate with othercomponents in the image processing device 610 via bus 615, to exchangedata and signals and coordinate various types of operations by imageprocessing device 610.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from non-transitory forms of computer-readable media. Suchinstructions can comprise, for example, instructions and data whichcause or otherwise configure a general purpose computer, special purposecomputer, or special purpose processing device to perform a certainfunction or group of functions.

Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example. The instructions, media for conveyingsuch instructions, computing resources for executing them, and otherstructures for supporting such computing resources are means forproviding the functions described in these disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents, elements, and/or operations described herein can beimplemented as software being stored on a tangible (non-transitory)computer-readable medium, devices, and memories (e.g.,disks/CDs/RAM/EEPROM, etc.) having program instructions executing on acomputer, hardware, firmware, or a combination thereof. Further, methodsdescribing the various functions and techniques described herein can beimplemented using computer-executable instructions that are stored orotherwise available from computer readable media. Such instructions cancomprise, for example, instructions and data which cause or otherwiseconfigure a general purpose computer, special purpose computer, orspecial purpose processing device to perform a certain function or groupof functions. Portions of computer resources used can be accessible overa network.

The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on. In addition, devices implementingmethods according to these disclosures can comprise hardware, firmwareand/or software, and can take any of a variety of form factors. Typicalexamples of such form factors include laptops, smart phones, small formfactor personal computers, personal digital assistants, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example. Instructions, media for conveyingsuch instructions, computing resources for executing them, and otherstructures for supporting such computing resources are means forproviding the functions described in these disclosures. Accordingly thisdescription is to be taken only by way of example and not to otherwiselimit the scope of the embodiments herein. Therefore, it is the objectof the appended claims to cover all such variations and modifications ascome within the true spirit and scope of the embodiments herein.

While the principles of this disclosure have been shown in variousembodiments, many modifications of structure, arrangements, proportions,elements, materials, and components, which are particularly adapted fora specific environment and operating requirements, may be used withoutdeparting from the principles and scope of this disclosure. These andother changes or modifications are intended to be included within thescope of the present disclosure.

Additionally, it is understood that any specific order or hierarchy ofsteps in the processes disclosed is an illustration of exemplaryapproaches. Based upon design preferences, it is understood that thespecific order or hierarchy of steps in the processes may be rearranged,or that only a portion of the illustrated steps be performed. Some ofthe steps may be performed simultaneously. For example, in certaincircumstances, multitasking and parallel processing may be advantageous.Moreover, the separation of various system components in the embodimentsdescribed above should not be understood as requiring such separation inall embodiments, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but are to be accorded the full scope consistentwith the language claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.”

A phrase such as an “aspect” does not imply that such aspect isessential to the subject technology or that such aspect applies to allconfigurations of the subject technology. A disclosure relating to anaspect may apply to all configurations, or one or more configurations. Aphrase such as an aspect may refer to one or more aspects and viceversa. A phrase such as a “configuration” does not imply that suchconfiguration is essential to the subject technology or that suchconfiguration applies to all configurations of the subject technology. Adisclosure relating to a configuration may apply to all configurations,or one or more configurations. A phrase such as a configuration mayrefer to one or more configurations and vice versa.

The word “exemplary” is used herein to mean “serving as an example orillustration.” Any aspect or design described herein as “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs. Those having skill in the art will appreciate thatmany changes may be made to the details of the above-describedembodiments without departing from the underlying principles of theinvention. The scope of the present invention should, therefore, bedetermined only by the following claims.

What is claimed is:
 1. An ultrasound imaging system, comprising: one ormore processors; a plurality of transducers coupled to the one or moreprocessors, wherein each transducer of the plurality of transducers hasa particular geometry configured for generating sonogram images for adifferent respective application; and a computer-readable medium coupledto the one or more processors, wherein the computer-readable mediumcomprises instructions stored therein, which when executed by the one ormore processors, cause the one or more processors to perform operationscomprising: receiving a selection of a first transducer from theplurality of transducers, wherein the first transducer is positioned ona surface of a patient's body above an anatomical region; automaticallyidentifying the anatomical region based on a signal received in responseto an activation of the first transducer; retrieving, from a database, afirst set of image parameters corresponding with the identifiedanatomical region; and configuring the first transducer based on thefirst set of image parameters; and collecting a first image from theidentified anatomical region via the first transducer, using the firstset of image parameters.
 2. The ultrasound imaging system of claim 1,wherein the one or more processors are further configured to performoperations comprising: updating the first set of image parameters togenerate a second set of image parameters, if it is determined thatadditional image settings need to be updated; configuring the firsttransducer based on the second set of image parameters; collecting asecond image from the identified anatomical region via the firsttransducer, suing the second set of image parameters; and analyzing oneor more properties of the second image to determine if any additionalimage parameters need to be updated.
 3. The ultrasound imaging system ofclaim 1, wherein the first set of image parameters comprises one or moredefault settings parameters corresponding with the first transducer andthe identified anatomical region.
 4. The ultrasound imaging system ofclaim 1, wherein the first set of image parameters comprises one or moreimage preference parameters corresponding with the identified anatomicalregion.
 5. The ultrasound imaging system of claim 1, further comprising:identifying one or more front-end parameters associated with the firsttransducer that need to be updated to improve sonogram images collectedusing the first transducer.
 6. The ultrasound imaging system of claim 1,further comprising: identifying one or more back-end parametersassociated that need to be updated to improve sonogram image outputquality.
 7. The ultrasound imaging system of claim 1, furthercomprising: identifying a reference image that has been approved by auser of the ultrasound imaging system; and comparing the first image tothe reference image to determine if any of the additional imageparameters need to be updated.
 8. The ultrasound imaging system of claim1, wherein automatically identifying the anatomical region is performedby a machine learning (ML) classifier.
 9. The ultrasound imaging systemof claim 8, wherein the ML classifier is an neural network.
 10. Acomputer-implemented method for optimizing sonogram images, the methodcomprising: receiving a selection of a first transducer from a pluralityof transducers, each transducer of the plurality of transducers having aparticular geometry configured for generating sonogram images for adifferent respective application, wherein the first transducer ispositioned over an anatomical region of a patient's body; automaticallyidentifying the anatomical region based on a signal received in responseto an activation of the first transducer; retrieving, from a database, afirst set of image parameters corresponding with the identifiedanatomical region; configuring the first transducer based on the firstset of image parameters; and collecting a first image from theidentified anatomical region via the first transducer, using the firstset of image parameters.
 11. The method of claim 10, further comprising:updating the first set of image parameters to generate a second set ofimage parameters, if it is determined that additional image settingsneed to be updated; configuring the first transducer based on the secondset of image parameters; collecting a second image from the identifiedanatomical region via the first transducer, suing the second set ofimage parameters; and analyzing one or more properties of the secondimage to determine if any additional image parameters need to beupdated.
 12. The method of claim 10, wherein the first set of imageparameters comprises one or more default settings parameterscorresponding with the first transducer and the identified anatomicalregion.
 13. The method of claim 10, wherein the first set of imageparameters comprises one or more image preference parameterscorresponding with the identified anatomical region.
 14. The method ofclaim 10, further comprising: identifying one or more front-endparameters associated with the first transducer that need to be updatedto improve sonogram images collected using the first transducer.
 15. Themethod of claim 10, further comprising: identifying one or more back-endparameters associated that need to be updated to improve sonogram imageoutput quality.
 16. The method of claim 10, further comprising:identifying a reference image that has been approved by a user of anultrasound imaging system; and comparing the first image to thereference image to determine if any additional image parameters need tobe updated.
 17. The method of claim 10, wherein automaticallyidentifying the anatomical region comprises automatically identifyingthe anatomical region using a machine learning (ML) classifier.
 18. Anon-transitory computer-readable storage medium comprising instructionsstored therein, which when executed by one or more processors, cause theone or more processors to perform operations comprising: receiving aselection of a first transducer from a plurality of transducers, eachtransducer of the plurality of transducers having a particular geometryconfigured for a respective different application, wherein the firsttransducer is positioned above an anatomical region of a patient's body;automatically identifying, using a machine learning (ML) classifier, theanatomical region based on a signal received in response to anactivation of the first transducer; retrieving, from a database, a firstset of image parameters corresponding with the identified anatomicalregion; configuring the first transducer based on the first set of imageparameters; collecting a first image from the identified anatomicalregion via the first transducer, using the first set of imageparameters; and analyzing one or more properties of the first image todetermine if any additional image parameters need to be updated.
 19. Thenon-transitory computer-readable storage medium of claim 18, wherein thefirst set of image parameters comprises one or more default settingsparameters corresponding with the first transducer and the identifiedanatomical region.
 20. The non-transitory computer-readable storagemedium of claim 18, wherein the first set of image parameters comprisesone or more image preference parameters corresponding with theidentified anatomical region.
 21. The non-transitory computer-readablestorage medium of claim 18, wherein analyzing the one or more propertiesof the first image to determine if any additional image parameters needto be updated, comprises: identifying one or more front-end parametersassociated with the first transducer that need to be updated to improvesonogram images collected using the first transducer.
 22. Thenon-transitory computer-readable storage medium of claim 18, whereinanalyzing the one or more properties of the first image to determine ifany additional image parameters need to be updated, comprises:identifying one or more back-end parameters associated that need to beupdated to improve sonogram image output quality.
 23. The non-transitorycomputer-readable storage medium of claim 18, further comprising:updating the first set of image parameters to generate a second set ofimage parameters, if it is determined that additional image settingsneed to be updated; configuring the first transducer based on the secondset of image parameters; collecting a second image from the identifiedanatomical region via the first transducer, suing the second set ofimage parameters; and analyzing one or more properties of the secondimage to determine if any additional image parameters need to beupdated.